code: def forward(self, x, thw_shape):
      B, N, _ = x.shape
  
      if self.pool_first:
          if self.mode == "conv_unshared":
              fold_dim = 1
          else:
              fold_dim = self.num_heads
          x = x.reshape(B, N, fold_dim, -1).permute(0, 2, 1, 3)
          q = k = v = x
      else:
          assert self.mode != "conv_unshared"
          if not self.separate_qkv:
              qkv = (
                  self.qkv(x)
                  .reshape(B, N, 3, self.num_heads, -1)
                  .permute(2, 0, 3, 1, 4)
              )
              q, k, v = qkv[0], qkv[1], qkv[2]
          else:
              q = k = v = x
              q = (
                  self.q(q)
                  .reshape(B, N, self.num_heads, -1)
                  .permute(0, 2, 1, 3)
              )
              k = (
                  self.k(k)
                  .reshape(B, N, self.num_heads, -1)
                  .permute(0, 2, 1, 3)
              )
              v = (
                  self.v(v)
                  .reshape(B, N, self.num_heads, -1)
                  .permute(0, 2, 1, 3)
              )
  
      q, q_shape = attention_pool(
          q,
          self.pool_q,
          thw_shape,
          has_cls_embed=self.has_cls_embed,
          norm=getattr(self, "norm_q", None),
      )
      k, k_shape = attention_pool(
          k,
          self.pool_k,
          thw_shape,
          has_cls_embed=self.has_cls_embed,
          norm=getattr(self, "norm_k", None),
      )
      v, v_shape = attention_pool(
          v,
          self.pool_v,
          thw_shape,
          has_cls_embed=self.has_cls_embed,
          norm=getattr(self, "norm_v", None),
      )
  
      if self.pool_first:
          q_N = (
              numpy.prod(q_shape) + 1
              if self.has_cls_embed
              else numpy.prod(q_shape)
          )
          k_N = (
              numpy.prod(k_shape) + 1
              if self.has_cls_embed
              else numpy.prod(k_shape)
          )
          v_N = (
              numpy.prod(v_shape) + 1
              if self.has_cls_embed
              else numpy.prod(v_shape)
          )
  
          q = q.permute(0, 2, 1, 3).reshape(B, q_N, -1)
          q = (
              self.q(q)
              .reshape(B, q_N, self.num_heads, -1)
              .permute(0, 2, 1, 3)
          )
  
          v = v.permute(0, 2, 1, 3).reshape(B, v_N, -1)
          v = (
              self.v(v)
              .reshape(B, v_N, self.num_heads, -1)
              .permute(0, 2, 1, 3)
          )
  
          k = k.permute(0, 2, 1, 3).reshape(B, k_N, -1)
          k = (
              self.k(k)
              .reshape(B, k_N, self.num_heads, -1)
              .permute(0, 2, 1, 3)
          )
  
      N = q.shape[2]
      attn = (q * self.scale) @ k.transpose(-2, -1)
      if self.rel_pos_spatial:
          attn = cal_rel_pos_spatial(
              attn,
              q,
              k,
              self.has_cls_embed,
              q_shape,
              k_shape,
              self.rel_pos_h,
              self.rel_pos_w,
          )
  
      if self.rel_pos_temporal:
          attn = cal_rel_pos_temporal(
              attn,
              q,
              self.has_cls_embed,
              q_shape,
              k_shape,
              self.rel_pos_t,
          )
      attn = attn.softmax(dim=-1)
  
      x = attn @ v
  
      if self.residual_pooling:
          if self.has_cls_embed:
              x[:, :, 1:, :] += q[:, :, 1:, :]
          else:
              x = x + q
  
      x = x.transpose(1, 2).reshape(B, -1, self.dim_out)
      x = self.proj(x)
  
      if self.drop_rate > 0.0:
          x = self.proj_drop(x)
      return x, q_shape
  
file_path: SlowFast/slowfast/models/attention.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  from slowfast.models.common import DropPath, Mlp
label_desc: Use image node_id 2 for calling the MultiScaleAttention obj's underlying member method code with example usage: obj.forward(x, thw_shape) and returns: x,  q_shape